Blood pressure (BP) is one of the most important contributing factors to pulse wave velocity (PWV), a classic measure of arterial stiffness. Although there have been many non-invasive studies to show the relation between arterial stiffness and BP, the results are controversial. The aim of this study is to evaluate the role of BP as an influencing factor on PWV using invasive method. We observed 174 normotensive and untreated hypertensive subjects using coronary angiography. Arterial stiffness was assessed through aorto-femoral PWV by foot-to-foot velocity method using fluid-filled system. And BP was measured by pressure wave at the right common femoral artery. From univariate analysis, age, diabetes mellitus (DM), hypertension, waist, waist-to-hip ratio, total cholesterol-tohigh-density lipoprotein cholesterol ratio, systolic BP (SBP), pulse pressure (PP) and mean arterial pressure (MAP) showed significant association with PWV. To avoid multiple colinearity among SBP, PP and MAP, we performed multiple regression analysis predicting PWV thrice. Age, DM and each BP were significantly and consistently correlated to PWV. In the first and third modules, compared to age, SBP and MAP were less strong predictors, respectively. However, PP was the stronger predictor than age and DM in the second module. Lastly, we simultaneously forced MAP and PP with other variables in the fourth multivariate analysis. Age, DM and PP remained significantly correlated with PWV, but the significance of MAP was lost. This is the first invasive study to suggest that PP has the strongest correlation with PWV among a variety of BP parameters.

BackgroundIn real practice, two or more antihypertensive drugs are needed to achieve target blood pressure. We investigated the comparative beneficial actions of combination therapy of renin-angiotensin system inhibitors (RASI), with calcium channel blockers (CCB) over CCB monotherapy on the development of new-onset diabetes mellitus (NODM) in Korean patients during four-year follow-up periods.MethodsA total of 3208 consecutive hypertensive patients without a history of diabetes mellitus who had been prescribed CCB were retrospectively enrolled from January 2004 to December 2012. These patients were divided into the two groups according to the additional use of RASI (the RASI group, n = 1221 and the no RASI group, n = 1987). Primary endpoint was NODM, defined as a fasting blood glucose ≥ 126 mg/dL or hemoglobin A1c ≥ 6.5%. Secondary endpoint was major adverse cardiac events (MACE) defined as total death, myocardial infarction (MI) and percutaneous coronary intervention (PCI).ResultsAfter propensity score-matched (PSM) analysis, two propensity-matched groups (939 pairs, n = 1878, C-statistic = 0.743) were generated. The incidences of NODM (HR = 1.009, 95% CI: 0.700–1.452, P = 0.962), MACE (HR = 0.877, 95% CI: 0.544–1.413, P = 0.589), total death, MI, PCI were similar between the two groups after PSM during four years.ConclusionsThe use of RASI in addition to CCB showed comparable incidences of NODM and MACE compared to CCB monotherapy in non-diabetic hypertensive Korean patients during four-year follow-up period. However, large-scaled randomized controlled clinical trials will be required for a more definitive conclusion.

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Introduction Cardiorespiratory fitness (CRF) is associated with a high risk of cardiovascular disease and all-cause mortality. The regression equation of American College of Sports Medicine (ACSM) was a preferred method for estimating maximal oxygen consumption (VO2max). It is well-known that CRF is overestimated in ACSM equation. Recently, Kokkinos reported more precise equation from the Fitness Registry and the Importance of Exercise National Database (FRIEND). Both equations were made from western healthy people. Purpose In this study, we compared VO2max estimated by ACSM and FRIEND equations to VO2max directly measured in male coronary artery disease (CAD) patients. Methods We analyzed 103 male CAD patients who underwent percutaneous coronary intervention and who participated in cardiac rehabilitation between June 2015 and December 2018. VO2max was directly measured by the gas exchange analysis during treadmill test with modified Bruce protocol. Exclusive criteria were pulmonary disease, chronic kidney disease on hemodialysis, malignancy, peripheral artery disease, insufficient cardiopulmonary exercise test and orthopedic injuries. Directly measured VO2max were compared to ACSM and FRIEND equations. Results Age-specific VO2max values, which were directly measured from male CAD patients, were shown in Table. Smaller CRF difference was shown in FRIEND equation than ACSM equation. Compared to the measured value, CRF estimated by ACSM equation was overestimated by 22%, but the one estimated by FRIEND equation had only 2% gap. Figure presents Bland-Altman plots. ACSM equation had the higher bias (5.52ml/kg/min) compared with FRIEND equation (0.200ml/kg/min). Comparison table of VO2max estimated by ACSM and FRIEND equations with directly measured VO2max in male CAD patients Age Number VO2max (ml/kg/min) Measured ACSM % predicted FRIEND % predicted 30–39 4 29.5 ± (6.6) 35.7 ± (6.1) 122.3 ± (8.5) 29.1 ± (4.3) 100.1 ± (8.7) 40–49 20 29.1 ± (5.1) 35.7 ± (5.4) 123.6 ± (11.1) 29.1 ± (3.8) 101.2 ± (9.5) 50–59 32 25.6 ± (4.3) 31.5 ± (5.1) 123.8 ± (10.1) 26.2 ± (3.6) 103.2 ± (8.5) 60–69 27 26.0 ± (5.1) 31.1 ± (2.6) 120.5 ± (12.6) 25.9 ± (4.0) 100.8 ± (10.5) 70–79 18 21.2 ± (6.0) 26.8 ± (5.4) 123.2 ± (14.8) 22.8 ± (3.9) 105.2 ± (12.0) >80 2 25.0 ± (10.1) 22.8 ± (2.6) 97 ± (28.9) 19.9 ± (1.9) 85.1 ± (27.0) Total 103 25.7 ± (5.6) 31.4 ± (6.0) 122.2 ± (12.4) 26.1 ± (4.3) 102.1 ± (10.4) Data are presented as mean ± (standard deviation). Bland-Altman plots Conclusions FRIEND equation can estimate CRF more accurately than ACSM equation, even in Asian patients with CAD.

A recent study has shown that quadriceps strength can be used to predict the level of exercise capacity in patients with coronary heart disease (CHD). We investigated whether the relationship between muscular strength and exercise capacity is also observed with hand grip strength (HGS). We studied 443 participants (age, 61.8±11.2 y; 77.7% male) who underwent coronary intervention and participated in cardiac rehabilitation between 2015 and 2018. Participants were assessed for grip strength, measured using a Jamar dynamometer. Logistic regression was used to assess the relationship between various clinical measures (HGS, age, sex, etc) with the distance walked on a 6-minute walk test (6MWT) and maximal oxygen uptake (VO2max). HGS was significantly related to distance walked on the 6MWT (r=0.435, p<0.001). It was the only predictor of all exercise capacity categories, and one of the strongest predictors of each exercise capacity category. A HGS of 25.5% of body weight predicted an achievement of a 200 m walk on the 6MWT (positive predictive value = 0.95). However, HGS less than 35.5% of body weight predicted that 500m could not be done in 6 minutes (negative predictive value = 0.97). This trend was also observed in the subgroups in which VO2max was measured. This study demonstrates that HGS is associated with exercise capacity in CHD and can be used to predict the level of exercise capacity. These findings may contribute to setting the recommended level of daily activity as well as the level of cardiac rehabilitation in CHD. Logistic regression models for different levels of exercise capacity Level of exercise capacity B±S.E p-value Odd ratio 95% CI Distance of 6MWT   200 m Grip strength 0.054±0.014 <0.001 1.056 1.027–1.086   300 m Grip strength 0.042±0.009 <0.001 1.042 1.024–1.062   400 m Grip strength 0.047±0.011 <0.001 1.048 1.026–1.070   500 m Grip strength 0.051±0.016 0.001 1.053 1.021–1.086 VO2max level   4 METs Grip strength 0.054±0.010 <0.001 1.056 1.036–1.076   6 METs Grip strength 0.059±0.011 <0.001 1.061 1.039–1.083   8 METs Grip strength 0.081±0.015 <0.001 1.085 1.053–1.117   10 METs Grip strength 0.113±0.049 0.019 1.12 1.019–3.232 Data are presented as mean ± standard deviation (SD). 6MWT, 6-minute walk test; STEMI, ST-Elevation Myocardial Infarction; SE, standard error; CI, confidence interval; VO2max, Maximal Oxygen uptake; METs, Metabolic equivalents.